The effect of estradiol on in vivo tumorigenesis is modulated by the human epidermal growth factor receptor 2/phosphatidylinositol 3-kinase/Akt1 pathway.

To determine whether the epidermal growth factor receptor 2 (ErbB2) and Akt1 can alter the in vivo growth of MCF-7 cells, parental cells or cells stably transfected with constitutively active Akt1 (myr-Akt1) or dominant-negative Akt1 mutants (K179M-Akt1 and R25C-Akt1) were implanted into athymic nude mice. Tumor growth was monitored in the presence or absence of the antiestrogen tamoxifen and the selective ErbB2 inhibitor, AG825. MCF-7 [parental or empty vector transfected, cytomegalovirus (CMV)] and myr-Akt1 cells formed tumors upon estradiol supplementation after 20-30 d (59-, 29-, and 17-fold increase in tumor volume, respectively). Tamoxifen and AG825 blocked the estradiol effect by 93 and 96% in MCF-7 xenografts, 88 and 81% in CMV xenografts, and 91% in myr-Akt1 xenografts. Furthermore, AG825 suppressed the growth of established tumors in CMV and myr-Akt1 inoculated animals by 68 and 75%, respectively, as compared with continued estrogen supplementation, suggesting a role for ErbB2. When K179M-Akt1 or R25C-Akt1 cells were injected into ovariectomized animals, tumor growth was reduced upon estradiol treatment by 95% and 98%, respectively, supporting a role for Akt1. In contrast to ovariectomized animals, in intact animals, myr-Akt1 cells could establish tumors without estradiol priming after 40-50 d (20-fold increase in tumor volume). Loss of Akt1 phosphorylation was associated with tumor growth inhibition. Immunohistochemical assays showed that in tumors from parental and CMV xenografts, estradiol decreased estrogen receptor-alpha expression and induced progesterone receptor expression and Akt phosphorylation, effects that were inhibited by tamoxifen, AG825, and R25C-Akt1 by 89, 82, and 77% for progesterone receptor expression and 48, 66, and 73% for pAkt expression, respectively. Cumulatively, our results suggest that Akt1 and ErbB2 are involved in in vivo tumorigenesis and modulation of estrogen receptor-alpha expression and activity.

Study of physiological responses to acute carbon monoxide exposure with a human patient simulator.

Human patient simulators are widely used to train health professionals and students in a clinical setting, but they also can be used to enhance physiology education in a laboratory setting. Our course incorporates the human patient simulator for experiential learning in which undergraduate university juniors and seniors are instructed to design, conduct, and present (orally and in written form) their project testing physiological adaptation to an extreme environment. This article is a student report on the physiological response to acute carbon monoxide exposure in a simulated healthy adult male and a coal miner and represents how 1) human patient simulators can be used in a nonclinical way for experiential hypothesis testing; 2) students can transition from traditional textbook learning to practical application of their knowledge; and 3) student-initiated group investigation drives critical thought. While the course instructors remain available for consultation throughout the project, the relatively unstructured framework of the assignment drives the students to create an experiment independently, troubleshoot problems, and interpret the results. The only stipulation of the project is that the students must generate an experiment that is physiologically realistic and that requires them to search out and incorporate appropriate data from primary scientific literature. In this context, the human patient simulator is a viable educational tool for teaching integrative physiology in a laboratory environment by bridging textual information with experiential investigation.

Bridging the divide between genomic science and indigenous peoples.

The new science of genomics endeavors to chart the genomes of individuals around the world, with the dual goals of understanding the role genetic factors play in human health and solving problems of disease and disability. From the perspective of indigenous peoples and developing countries, the promises and perils of genomic science appear against a backdrop of global health disparity and political vulnerability. These conditions pose a dilemma for many communities when attempting to decide about participating in genomic research or any other biomedical research. Genomic research offers the possibility of improved technologies for managing the acute and chronic diseases that plague their members. Yet, the history of particularly biomedical research among people in indigenous and developing nations offers salient examples of unethical practice, misuse of data, and failed promises. This dilemma creates risks for communities who decide either to participate or not to participate in genomic science research. Some argue that the history of poor scientific practice justifies refusal to join genomic research projects. Others argue that disease poses such great threats to the well-being of people in indigenous communities and developing nations that not participating in genomic research risks irrevocable harm. Thus, some communities particularly among indigenous peoples have declined to participate as subjects in genomic research. At the same time, some communities have begun developing new guidelines, procedures, and practices for engaging with the scientific community that offer opportunities to bridge the gap between genomic science and indigenous and/or developing communities. Four new approaches warrant special attention and further support: consulting with local communities; negotiating the complexities of consent; training members of local communities in science and health care; and training scientists to work with indigenous communities. Implicit is a new definition of "rigorous scientific research," one that includes both community development and scientific progress as legitimate objectives of genomic research. Innovative translational research is needed to develop practical, mutually acceptable methods for crossing the divide between genomic researchers and indigenous communities. This may mean the difference between success and failure in genomic science, and in improving health for all peoples.

Improved Statistical Methods are Needed to Advance Personalized Medicine.

Common methods of statistical analysis, e.g. Analysis of Variance and Discriminant Analysis, are not necessarily optimal in selecting therapy for an individual patient. These methods rely on group differences to identify markers for disease or successful interventions and ignore sub-group differences when the number of sub-groups is large. In these circumstances, they provide the same advice to an individual as the average patient. Personalized medicine needs new statistical methods that allow treatment efficacy to be tailored to a specific patient, based on a large number of patient characteristics. One such approach is the sequential k-nearest neighbor analysis (patients-like-me algorithm). In this approach, the k most similar patients are examined sequentially until a statistically significant conclusion about the efficacy of treatment for the patient-at-hand can be arrived at. For some patients, the algorithm stops before the entire set of data is examined and provides beneficial advice that may contradict recommendations made to the average patient. Many problems remain in creating statistical tools that can help individual patients but this is an important area in which progress in statistical thinking is helpful.